gistfile1.txt

## -----------------------------------------------------------------------------------------------------------
library(maps)
data(us.cities)
head(us.cities)

map(database = "usa") 
capitals <- subset(us.cities, capital == 2) # subset state capitals

## add points proportional to population using latitude and longitude
points(x = capitals$long, y = capitals$lat,
       cex = capitals$pop / 500000, pch = 19) 
title("US state capitals") # add a title

map(database = "state", regions = "California")

cal.cities <- subset(us.cities, subset = (country.etc == "CA"))
sind <- order(cal.cities$pop, decreasing = TRUE) # order by population
top7 <- sind[1:7] # seven cities with largest population

map(database = "state", regions = "California")

points(x = cal.cities$long[top7], y = cal.cities$lat[top7], pch = 19)

## add a constant to latitude to avoid overlapping with circles
text(x = cal.cities$long[top7] + 2.25, y = cal.cities$lat[top7],
     label = cal.cities$name[top7])
title("Largest cities of California")

usa <- map(database = "usa", plot = FALSE) # save map
names(usa)  # list elements

length(usa$x) 
head(cbind(usa$x, usa$y)) # first five coordinates of a polygon


## -----------------------------------------------------------------------------------------------------------
allcolors <- colors()

head(allcolors)   # some colors
length(allcolors) # number of color names

red <- rgb(red = 1, green = 0, blue = 0) # red
green <- rgb(red = 0, green = 1, blue = 0) # green
blue <- rgb(red = 0, green = 0, blue = 1) # blue
c(red, green, blue) # results

black <- rgb(red = 0, green = 0, blue = 0) # black
white <- rgb(red = 1, green = 1, blue = 1) # white
c(black, white) # results

rgb(red = c(0.5, 1), green = c(0, 1), blue = c(0.5, 0))

## semi-transparent blue
blue.trans <- rgb(red = 0, green = 0, blue = 1, alpha = 0.5) 

## semi-transparent black
black.trans <- rgb(red = 0, green = 0, blue = 0, alpha = 0.5) 

## completely colored dots; difficult to distinguish
plot(x = c(1, 1), y = c(1, 1.2), xlim = c(0.5, 4.5), ylim = c(0.5, 4.5), 
     pch = 16, cex = 5, ann = FALSE, col = black)
points(x = c(3, 3), y = c(3, 3.2), pch = 16, cex = 5, col = blue)

## semi-transparent; easy to distinguish
points(x = c(2, 2), y = c(2, 2.2), pch = 16, cex = 5, col = black.trans) 
points(x = c(4, 4), y = c(4, 4.2), pch = 16, cex = 5, col = blue.trans) 


## -----------------------------------------------------------------------------------------------------------
pres08 <- read.csv("pres08.csv")
## two-party vote share
pres08$Dem <- pres08$Obama / (pres08$Obama + pres08$McCain)
pres08$Rep <- pres08$McCain / (pres08$Obama + pres08$McCain)

## color for California
cal.color <- rgb(red = pres08$Rep[pres08$state == "CA"], 
                 blue = pres08$Dem[pres08$state == "CA"], 
                 green = 0)

## California as a blue state
map(database = "state", regions = "California", col = "blue", 
    fill = TRUE)

## California as a purple state
map(database = "state", regions = "California", col = cal.color,
    fill = TRUE)

## America as red and blue states
map(database = "state") # create a map
map(database = "state") # for some reason this needs to be repeated twice to get the map correct if you run markdown
for (i in 1:nrow(pres08)) {
    if ((pres08$state[i] != "HI") & (pres08$state[i] != "AK") &
        (pres08$state[i] != "DC")) {
        maps::map(database = "state", regions = pres08$state.name[i],
            col = ifelse(pres08$Rep[i] > pres08$Dem[i], "red", "blue"),
            fill = TRUE, add = TRUE)
    }
}

## America as purple states
map(database = "state") # create a map
for (i in 1:nrow(pres08)) {
    if ((pres08$state[i] != "HI") & (pres08$state[i] != "AK") &
        (pres08$state[i] != "DC")) {
        map(database = "state", regions = pres08$state.name[i], 
            col = rgb(red = pres08$Rep[i], blue = pres08$Dem[i],
                green = 0), fill = TRUE, add = TRUE)
    }
}


## -----------------------------------------------------------------------------------------------------------
walmart <- read.csv("walmart.csv")

## red = WalMartStore, green = SuperCenter, blue = DistributionCenter
walmart$storecolors <- NA # create an empty vector

walmart$storecolors[walmart$type == "Wal-MartStore"] <- 
    rgb(red = 1, green = 0, blue = 0, alpha = 1/3)
walmart$storecolors[walmart$type == "SuperCenter"] <-
    rgb(red = 0, green = 1, blue = 0, alpha = 1/3)
walmart$storecolors[walmart$type == "DistributionCenter"] <-
    rgb(red = 0, green = 0, blue = 1, alpha = 1/3)

## larger circles for DistributionCenter
walmart$storesize <- ifelse(walmart$type == "DistributionCenter", 1, 0.5)

## map with legend
map(database = "state")

points(walmart$long, walmart$lat, col = walmart$storecolors, 
       pch = 19, cex = walmart$storesize)

legend(x = -120, y = 32, bty = "n", 
       legend = c("Wal-Mart", "Supercenter", "Distrib. Center"),
       col = c("red", "green", "blue"), pch = 19, # solid circles
       pt.cex = c(0.5, 0.5, 1)) # size of circles

### Section 5.3.6: Animation in R

walmart.map <- function(data, date) {
    walmart <- subset(data, subset = (opendate <= date))
    map(database = "state")
    points(walmart$long, walmart$lat, col = walmart$storecolors, 
           pch = 19, cex = walmart$storesize)
}

walmart$opendate <- as.Date(walmart$opendate)

walmart.map(walmart, as.Date("1974-12-31"))
title("1975")

walmart.map(walmart, as.Date("1984-12-31"))
title("1985")

walmart.map(walmart, as.Date("1994-12-31"))
title("1995")

walmart.map(walmart, as.Date("2004-12-31"))
title("2005")

n <- 25 # number of maps to animate
dates <- seq(from = min(walmart$opendate), 
             to = max(walmart$opendate), length.out = n)
## library("animation")
## saveHTML({
##     for (i in 1:length(dates)) {
##         walmart.map(walmart, dates[i])
##         title(dates[i])
##     }
## }, title = "Expansion of Walmart", htmlfile = "walmart.html",
##          outdir = getwd(), autobrowse = FALSE)